CN114386013A

CN114386013A - Automatic student status authentication method and device, computer equipment and storage medium

Info

Publication number: CN114386013A
Application number: CN202210029895.6A
Authority: CN
Inventors: 欧阳高询
Original assignee: Ping An Life Insurance Company of China Ltd
Current assignee: Ping An Life Insurance Company of China Ltd
Priority date: 2022-01-12
Filing date: 2022-01-12
Publication date: 2022-04-22

Abstract

The embodiment of the application belongs to the field of artificial intelligence and relates to an automatic student status authentication method which comprises the steps of obtaining a picture to be identified, preprocessing the picture to be identified and obtaining a preprocessed picture; extracting text information and a verification code picture in the preprocessed picture, acquiring a verification code classification model, and classifying and identifying the verification code picture in the preprocessed picture according to the verification code classification model to obtain a verification code class of the verification code picture; matching a corresponding verification code extraction model according to the verification code category, and extracting verification code information in a verification code picture according to the verification code extraction model; and acquiring a network address of the target website, jumping to the target website according to the network address, logging in the target website based on the verification code information and the text information, and automatically verifying the student status information according to the target website. The application also provides a student status automatic authentication device, computer equipment and a storage medium. The captcha information may be stored in a block chain. The method and the device improve the efficiency of the school roll authentication.

Description

Automatic student status authentication method and device, computer equipment and storage medium

Technical Field

The present application relates to the field of artificial intelligence technologies, and in particular, to a method and an apparatus for automatically authenticating a student status, a computer device, and a storage medium.

Background

Currently, with the rapid development of information technology, the information to be processed is increasing. Especially, when verifying large-scale student status information, a large amount of data is required to be processed simultaneously. In the traditional student status authentication, the identity information of a user needs to be manually input, the user logs in to a target website, and the student status of the user is authenticated through the target website. However, when a large number of users need to be authenticated at the same time, the problem of processing too slow and even website breakdown occurs.

Disclosure of Invention

An embodiment of the present application provides a method, an apparatus, a computer device, and a storage medium for automatic authentication of a student status, so as to solve the technical problem of low authentication efficiency of the student status.

In order to solve the above technical problem, an embodiment of the present application provides an automatic authentication of a student status, which adopts the following technical solutions:

acquiring a picture to be identified, and preprocessing the picture to be identified to obtain a preprocessed picture;

extracting text information and a verification code picture in the preprocessed picture, acquiring a trained verification code classification model, and classifying and identifying the verification code picture in the preprocessed picture according to the verification code classification model to obtain a verification code category of the verification code picture;

matching a corresponding verification code extraction model according to the verification code category, and extracting verification code information in the verification code picture according to the verification code extraction model;

and acquiring a network address of a target website, skipping to the target website according to the network address, logging in to the target website based on the verification code information and the text information, and automatically verifying the student status information in the text information according to the target website.

Further, the step of preprocessing the picture to be recognized to obtain a preprocessed picture includes:

acquiring a preset form line identification model, and identifying form line information in the picture to be identified based on the form line identification model;

and screening the table line information from the picture to be identified to obtain the preprocessed picture.

Further, the step of identifying table line information in the picture to be identified based on the table line identification model comprises:

detecting a candidate area of the table line in the picture to be identified based on the table line identification model to obtain an optimal candidate frame;

inputting the preferred candidate frame to a residual error network in the table line identification model for feature calculation to obtain a feature value of each convolution layer in the residual error network, and constructing a feature pyramid based on the feature values;

and performing regression detection through the feature pyramid and the preferred candidate frame to obtain table line information in the picture to be identified.

Further, after the step of performing regression detection on the feature pyramid and the preferred candidate frame to obtain table line information in the picture to be identified, the method further includes:

and acquiring a preset edge line detection algorithm, and fitting the form line information according to the edge line detection algorithm to obtain the target form line information of the picture to be recognized.

Further, the step of extracting the text information in the preprocessed picture includes:

performing image text recognition on the preprocessed picture to obtain a coarse text;

and acquiring a stored text dictionary, and correcting and structuring the coarse text based on the text dictionary to obtain the text information.

Further, before the step of classifying and identifying the verification code picture in the preprocessed picture according to the verification code classification model, the method further includes:

acquiring a time stamp of the verification code picture, determining whether the verification code picture is valid according to the time stamp, and determining that the verification code picture is a valid picture if the time stamp is within a preset validity range;

and if the time stamp is not in the preset validity period range, determining that the verification code picture is an invalid picture.

Further, before the step of obtaining the trained captcha classification model, the method further includes:

collecting a plurality of groups of website verification code pictures, and classifying the website verification code pictures to obtain classification labels;

and training a preset basic verification classification model according to the classification label and the website verification code picture, and determining the trained basic verification classification model as the verification code classification model when the basic verification classification model is trained.

In order to solve the above technical problem, an embodiment of the present application further provides an automatic student status authentication apparatus, which adopts the following technical solutions:

the device comprises a preprocessing module, a recognition module and a recognition module, wherein the preprocessing module is used for acquiring a picture to be recognized and preprocessing the picture to be recognized to obtain a preprocessed picture;

the classification module is used for extracting text information and verification code pictures in the preprocessed pictures, acquiring a trained verification code classification model, and classifying and identifying the verification code pictures in the preprocessed pictures according to the verification code classification model to obtain verification code classes of the verification code pictures;

the matching module is used for matching a corresponding verification code extraction model according to the verification code category and extracting verification code information in the verification code picture according to the verification code extraction model;

and the verification module is used for acquiring a network address of a target website, jumping to the target website according to the network address, logging in the target website based on the verification code information and the text information, and automatically verifying the student status information in the text information according to the target website.

In order to solve the above technical problem, an embodiment of the present application further provides a computer device, which adopts the following technical solutions:

In order to solve the above technical problem, an embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solutions:

According to the automatic student status authentication method, the picture to be identified is obtained and preprocessed to obtain a preprocessed picture, and the accuracy of picture verification and classification can be improved by preprocessing the picture; then extracting text information and a verification code picture in the preprocessed picture, acquiring a trained verification code classification model, and classifying and identifying the verification code picture in the preprocessed picture according to the verification code classification model to obtain a verification code class of the verification code picture; the corresponding verification code extraction model is matched according to the verification code category, and the verification code information in the verification code picture is extracted according to the verification code extraction model, so that the accuracy of verification code information extraction is improved, and errors of subsequent student status login are avoided; and then, acquiring a network address of the target website, jumping to the target website according to the network address, logging in the target website based on the verification code information and the text information, and automatically verifying the student status information in the text information according to the target website, so that the end-to-end intelligent authentication of the student status is realized, the efficiency and the accuracy of the student status authentication are improved, and the authentication duration when a large batch of student status is authenticated is reduced.

Drawings

In order to more clearly illustrate the solution of the present application, the drawings needed for describing the embodiments of the present application will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and that other drawings can be obtained by those skilled in the art without inventive effort.

FIG. 1 is an exemplary system architecture diagram in which the present application may be applied;

fig. 2 is a flow chart of one embodiment of a method for automatic authentication of a cadaver according to the present application;

fig. 3 is a schematic structural diagram of an embodiment of an automatic membership authentication apparatus according to the present application;

FIG. 4 is a schematic block diagram of one embodiment of a computer device according to the present application.

Reference numerals: the automatic student status authentication device 300, a preprocessing module 301, a classification module 302, a matching module 303 and a verification module 304.

Detailed Description

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "including" and "having," and any variations thereof, in the description and claims of this application and the description of the above figures are intended to cover non-exclusive inclusions. The terms "first," "second," and the like in the description and claims of this application or in the above-described drawings are used for distinguishing between different objects and not for describing a particular order.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.

In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

As shown in fig. 1, the system architecture 100 may include

terminal devices

101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the

terminal devices

101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.

The user may use the

terminal devices

101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The

terminal devices

101, 102, 103 may have various communication client applications installed thereon, such as a web browser application, a shopping application, a search application, an instant messaging tool, a mailbox client, social platform software, and the like.

The

terminal devices

101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture experts Group Audio Layer III, mpeg compression standard Audio Layer 3), MP4 players (Moving Picture experts Group Audio Layer IV, mpeg compression standard Audio Layer 4), laptop portable computers, desktop computers, and the like.

The server 105 may be a server providing various services, such as a background server providing support for pages displayed on the

terminal devices

101, 102, 103.

It should be noted that the automatic student status authentication method provided in the embodiment of the present application is generally executed by a server/terminal device, and accordingly, the automatic student status authentication apparatus is generally disposed in the server/terminal device.

It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

With continued reference to fig. 2, a flow diagram of one embodiment of a method for automatic authentication of a cadaver according to the present application is shown. The automatic student status authentication method comprises the following steps:

step S201, a picture to be identified is obtained, and the picture to be identified is preprocessed to obtain a preprocessed picture.

In this embodiment, the picture to be processed is a record list picture including account information, student status information, student history information, and verification code picture information of the user, and when the picture to be identified is received, the picture to be processed is preprocessed to obtain a preprocessed picture. The preprocessing comprises the steps of removing table lines and up-sampling and the like of the picture to be recognized.

Step S202, extracting text information and verification code pictures in the preprocessed pictures, obtaining trained verification code classification models, and classifying and identifying the verification code pictures in the preprocessed pictures according to the verification code classification models to obtain verification code classes of the verification code pictures.

In this embodiment, when obtaining the preprocessed image, ocr (optical character recognition) recognition is performed on the preprocessed image to obtain the text information and the verification code image in the preprocessed image. The text information comprises account information, student status information and student calendar information of a plurality of users, and the verification code picture comprises verification code information of a target website (such as a student communication network). And acquiring a trained verification code classification model, and classifying and identifying the verification code picture according to the verification code classification model to obtain a verification code class corresponding to the verification code picture, wherein the verification code class comprises different classes such as letters, characters and the like. The verification code classification model can adopt a target detection convolutional neural network (such as RCNN (region with CNN feature), and the verification code picture in the preprocessed picture is detected through the target detection convolutional neural network, so that the type of the verification code picture is identified and obtained.

And step S203, matching a corresponding verification code extraction model according to the type of the verification code, and extracting verification code information in the verification code picture according to the verification code extraction model.

In this embodiment, the verification code extraction model is a pre-trained extraction model, and according to the verification code extraction model, verification code information in a verification code picture can be automatically extracted. Since the complexity of the verification code information of different types is different, if the verification code information of different types is extracted through the same verification code extraction model, the verification code information obtained by extraction is not accurate enough. Therefore, different identifying code types are associated with different identifying code extraction models in advance, when the identifying code type of the current identifying code picture is obtained, the corresponding identifying code extraction model is matched according to the identifying code type, and different identifying code types correspond to different identifying code extraction models. And extracting the verification code information in the verification code picture based on the verification code extraction model. Specifically, the verification code extraction model may adopt a deep learning model of CTPN + CRNN (scene text detection + convolutional recurrent neural network), collect a plurality of groups of verification codes of different categories as recognition training pictures, and train the general CTPN + CRNN deep learning model according to the recognition training pictures of the different categories, so as to obtain a verification code extraction model corresponding to each verification code category. Therefore, the accuracy of identifying the verification code can be improved through the verification code extraction model corresponding to the category.

It is emphasized that, to further ensure the privacy and security of the authentication code information, the authentication code information may also be stored in a node of a block chain.

The block chain referred by the application is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.

Step S204, a network address of a target website is obtained, the target website is jumped to according to the network address, the target website is logged in based on the verification code information and the text information, and the automatic verification is carried out on the subject information in the text information according to the target website.

In this embodiment, when the verification code information in the verification code picture is obtained, the network address of the target website is obtained, and the target website is automatically jumped to according to the network address; and inputting the account information in the verification code information and the text information to a login interface of the target website, namely realizing automatic login of the target website. After logging in the target website, the real subject information and the real calendar information of the target website can be inquired, and the subject information and the calendar information in the text information are verified according to the real subject information and the real calendar information so as to determine whether the subject and the calendar of the current user pass the verification; if the real subject information and the real student calendar information are successfully matched with the subject information and the student calendar information in the text information, the fact that the subject information and the student calendar of the current user pass verification is determined; and if any one of the real subject information and the real subject information is failed to be matched with the subject information and the subject information in the text information, determining that the subject or the subject corresponding to the current user is failed to be verified.

The method and the device have the advantages that the intelligent authentication of the student status from end to end is realized, the efficiency and the accuracy of the student status authentication are improved, and the authentication duration when large batches of student status are authenticated is reduced.

In some optional implementation manners of this embodiment, the step of preprocessing the picture to be recognized to obtain a preprocessed picture includes:

In this embodiment, when the picture to be recognized is preprocessed, the table line of the picture to be recognized may be removed. Specifically, a preset form line identification model is obtained, and form line information in the picture to be identified is identified according to the form line identification model. The table line recognition model is a pre-trained residual error network model (such as a resnet100+ residual error network model), the residual error network model is formed by cascading a plurality of convolution layers through residual error blocks, and the calculation accuracy of the model can be improved while the calculation amount is reduced through the residual error network model. When a picture to be recognized is obtained, inputting the picture to be recognized into the form line recognition model, and recognizing and obtaining form line information in the picture to be recognized based on the form line recognition model; and then, screening the table line information from the picture to be recognized to obtain a preprocessed picture corresponding to the picture to be recognized.

In the embodiment, after the table line information in the picture to be recognized is processed through the preset table line recognition model, the table line information is removed from the picture to be recognized to obtain the preprocessed picture, so that the efficient preprocessing of the picture is realized, and the accuracy of the subsequent processing of the picture is further improved.

In some optional implementation manners of this embodiment, the step of identifying the table line information in the picture to be identified based on the table line identification model includes:

In this embodiment, when the form line identification model is obtained, the picture to be identified is input into the form line identification model, and a candidate region of the form line in the picture to be identified is detected and obtained, where the candidate region includes a plurality of candidate frames with different sizes and different areas; then, the Non-Maximum value calculation (NMS) is performed on the candidate box through the normalization layer (i.e., softmax-NMS layer) of the table line recognition model to obtain the score corresponding to each candidate box, such as < xi, yi, w, h, secret >. And then, carrying out Gaussian weighting on the score corresponding to each candidate frame to obtain a weighted value, sorting each candidate frame based on the weighted value, and screening out the candidate frames with the weighted values smaller than a preset threshold value as preferred candidate frames. When the preferred candidate frame is obtained, the preferred candidate frame is input to a basic residual error network (such as resnet50) of the table line identification model, the feature value of each convolution network layer corresponding to the preferred candidate frame is calculated according to the basic residual error network, and then feature fusion is performed on the feature value corresponding to each layer to obtain a feature pyramid. Inputting the preferred candidate frame and the feature pyramid to a frame classification and regression detection network of a table line identification model, and calculating to obtain a regression detection result; and finally, pooling the regression detection result, for example, pooling the regression detection result through roiign (regional feature aggregation) to obtain table line information in the picture to be identified.

In the embodiment, the table line information in the picture to be recognized is detected through the table line recognition model, so that the table line is accurately detected, and the detection efficiency of the table line is improved.

In some optional implementation manners of this embodiment, after the step of performing regression detection through the feature pyramid and the preferred candidate frame to obtain table line information in the picture to be recognized, the method further includes:

In this embodiment, in order to obtain more accurate table line information, when the table line information in the picture to be recognized is obtained through the target table line detection model, a preset edge line detection algorithm is obtained, and the table line information is fitted according to the edge line detection algorithm to obtain the target table line information. The target table line information is more accurate table line information. Specifically, the edge line detection algorithm is a hough algorithm, when table line information in a picture to be identified is obtained, whether the picture to be identified is a gray image or not is determined, and if the picture to be identified is not the gray image, the picture to be identified is converted into the gray image; denoising the gray level image to obtain a denoised image; performing edge extraction on the denoised image through a gradient operator or a Laplace operator to obtain edge points; mapping the edge point to a Hough space, calculating a local maximum value of the edge point, and filtering the local maximum value through a preset filtering threshold value to obtain a target coordinate; drawing a straight line according to the target coordinates, and calibrating angular points to obtain edge line information; and fitting the table line information according to the edge line information to obtain target table line information.

In the embodiment, the table line information is fitted through the edge line detection algorithm, so that the table line information is optimized to be extracted, and the target table line information obtained from the picture to be recognized is more accurate.

In some optional implementation manners of this embodiment, the step of extracting the text information in the preprocessed picture includes:

In this embodiment, the text dictionary is a dictionary in which various types of standard field information are stored in advance. And when the preprocessed picture is obtained, carrying out image text recognition, namely OCR recognition on the picture to be processed to obtain a coarse text in the picture to be processed. Acquiring a stored text dictionary, and matching standard field information in the dictionary with the coarse text to obtain a matched field; and acquiring matching information corresponding to the matching field in the rough text, and structuring the matching information according to the matching field to obtain text information corresponding to the preprocessed picture. Further, in order to obtain the text information more accurately, when obtaining the coarse text, the coarse text may be corrected first, and then the corrected coarse text is structured to obtain the text information.

Specifically, a preset regular matching formula is obtained, wherein the regular matching formula includes a plurality of different reference fields. And matching the regular matching formula and the coarse text, and determining that the regular matching formula and the coarse text fail to be matched when the matching degree of the regular matching formula and the coarse text is less than or equal to a preset threshold value. Acquiring a text which fails to be matched, wherein the text which fails to be matched is an error correction text, and searching the error correction text according to a text dictionary to obtain a correct text corresponding to the error correction text; and replacing the error correction text in the coarse text with the correct text to obtain the error-corrected coarse text. And finally, structuring the corrected coarse text according to the matching field to obtain more accurate text information.

According to the embodiment, the text information is obtained by carrying out image text recognition and structuralization on the preprocessed picture, and efficient and accurate extraction of the text information in the preprocessed picture is realized.

In some optional implementation manners of this embodiment, before the step of performing classification and identification on the verification code picture in the preprocessed pictures according to the verification code classification model, the method further includes:

In this embodiment, before the verification code pictures in the preprocessed pictures are classified and identified according to the verification code classification model, a timestamp of each verification code picture is obtained, where the timestamp is time information corresponding to each verification code picture. Acquiring a preset validity range, and if the timestamp is within the preset validity range, determining the verification code picture as a valid picture; and if the time stamp is not within the preset validity period range, determining that the verification code picture is an invalid picture. When the verification code picture is an effective picture, classifying and identifying the verification code picture in the preprocessed picture according to a verification code classification model; and when the verification code picture is an invalid picture, feeding back invalid information of the verification code picture.

In the embodiment, the time stamp of the verification code picture is obtained, and whether the verification code picture is a valid picture is determined according to the time stamp, so that the picture processing time is saved, and invalid login of the student status caused by invalid verification of the verification code picture is avoided.

In some optional implementations of this embodiment, before the step of obtaining the trained captcha classification model, the method further includes:

In this embodiment, a plurality of sets of website verification code pictures are collected, and the website verification code pictures are classified to obtain classification tags corresponding to each website verification code picture, such as different types of verification code tags, such as text verification codes and letter verification codes. Inputting the classification label and the website verification code picture into a basic verification classification model, training the basic verification classification model, and determining the basic verification classification model as the trained verification code classification model when the basic verification classification model is trained. Specifically, the acquired website verification code picture is used as a positive sample of the basic verification model, another group of pictures not including the verification code is acquired, the pictures not including the verification code are used as a negative sample, and the negative sample and the positive sample form a training picture. Inputting the training picture into a basic verification classification model, detecting the input training picture according to the Faster-RCNN backbone network of the basic verification classification model to form a candidate area of a verification code, then grading the candidate area to obtain a grading result, and carrying out Gaussian weighting on the grading result to obtain a final weighting value. And screening the weighted value according to a preset threshold value, and selecting the candidate area with the weighted value being greater than or equal to the preset threshold value as an optimal selection frame. When the preferred frame is obtained, the preferred frame is input into a basic residual error network (such as resnet50), the feature value of each convolutional network layer in the basic residual error network is calculated according to the preferred frame, and then the feature values corresponding to each layer are fused to obtain a feature pyramid. Inputting the preferred frame and the feature pyramid to a frame classification and regression detection network, calculating to obtain a regression detection result, and pooling the regression detection result to obtain a verification classification result.

When the verification classification result is obtained, obtaining a classification label corresponding to the training picture, and calculating to obtain a loss function based on the verification classification result and the classification label; and adjusting the network parameters of the basic verification classification model through the loss function until the loss function obtained through the network parameter calculation of the adjusted basic verification classification model is converged, and determining the adjusted basic verification classification model as a trained verification code classification model.

In the embodiment, the verification code classification model is obtained by training the basic verification classification model, so that the verification code pictures can be efficiently classified through the verification code classification model, and the efficiency and the accuracy of the student status authentication are further improved.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware associated with computer readable instructions, which can be stored in a computer readable storage medium, and when executed, the processes of the embodiments of the methods described above can be included. The storage medium may be a non-volatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a Random Access Memory (RAM).

It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

With further reference to fig. 3, as an implementation of the method shown in fig. 2, the present application provides an embodiment of an automatic membership authentication apparatus, which corresponds to the embodiment of the method shown in fig. 2, and which can be applied to various electronic devices.

As shown in fig. 3, the automatic membership authentication apparatus 300 according to the present embodiment includes: a preprocessing module 301, a classification module 302, a matching module 303, and a verification module 304. Wherein:

the preprocessing module 301 is configured to acquire a picture to be identified, and preprocess the picture to be identified to obtain a preprocessed picture;

in some optional implementations of this embodiment, the preprocessing module 301 includes:

the first identification unit is used for acquiring a preset table line identification model and identifying the table line information in the picture to be identified based on the table line identification model;

and the screening unit is used for screening the table line information from the picture to be identified to obtain the preprocessed picture.

The classification module 302 is configured to extract text information and a verification code picture in the preprocessed picture, acquire a trained verification code classification model, and perform classification and identification on the verification code picture in the preprocessed picture according to the verification code classification model to obtain a verification code category of the verification code picture;

in some optional implementations of this embodiment, the classification module 302 includes:

the first detection unit is used for detecting a candidate area of the table line in the picture to be identified based on the table line identification model to obtain an optimal candidate frame;

the calculation unit is used for inputting the preferred candidate frame to a residual error network in the table line identification model for feature calculation to obtain a feature value of each convolution layer in the residual error network, and constructing a feature pyramid based on the feature values;

and the second detection unit is used for performing regression detection through the characteristic pyramid and the preferred candidate frame to obtain table line information in the picture to be identified.

In some optional implementation manners of this embodiment, the second detection unit further includes:

and the fitting unit is used for acquiring a preset edge line detection algorithm, and fitting the form line information according to the edge line detection algorithm to obtain the target form line information of the picture to be recognized.

In some optional implementations of this embodiment, the classification module 302 further includes:

the second identification unit is used for carrying out image text identification on the preprocessed picture to obtain a coarse text;

and the error correction unit is used for acquiring a stored text dictionary, correcting and structuring the coarse text based on the text dictionary and obtaining the text information.

The matching module 303 is configured to match a corresponding verification code extraction model according to the type of the verification code, and extract verification code information in the verification code picture according to the verification code extraction model;

The verification module 304 is configured to obtain a network address of a target website, jump to the target website according to the network address, log in to the target website based on the verification code information and the text information, and automatically verify the cadastral information in the text information according to the target website.

In some optional implementations of the present embodiment, the automatic membership authentication apparatus 300 further includes:

the acquisition module is used for acquiring a time stamp of the verification code picture, determining whether the verification code picture is valid according to the time stamp, and determining that the verification code picture is a valid picture if the time stamp is within a preset validity range;

and the confirmation module is used for determining that the verification code picture is an invalid picture if the timestamp is not in the preset validity range.

the system comprises an acquisition module, a classification module and a display module, wherein the acquisition module is used for acquiring a plurality of groups of website verification code pictures and classifying the website verification code pictures to obtain classification labels;

and the training module is used for training a preset basic verification classification model according to the classification labels and the website verification code pictures, and when the basic verification classification model is trained, determining the trained basic verification classification model as the verification code classification model.

In this embodiment, a plurality of sets of website verification code pictures are collected, and the website verification code pictures are classified to obtain classification tags corresponding to each website verification code picture, such as different types of verification code tags, such as text verification codes and letter verification codes. Inputting the classification label and the website verification code picture into a basic verification classification model, training the basic verification classification model, and determining the basic verification classification model as the trained verification code classification model when the basic verification classification model is trained. Specifically, the acquired website verification code picture is used as a positive sample of the basic verification model, another group of pictures not including the verification code is acquired, the pictures not including the verification code are used as a negative sample, and the negative sample and the positive sample form a training picture. Inputting the training picture into a basic verification classification model, detecting the input training picture according to the Faster-RCNN backbone network of the basic verification classification model to form a candidate area of a verification code, then grading the candidate area to obtain a grading result, and carrying out Gaussian weighting on the grading result to obtain a final weighting value. And screening the weighted value according to a preset threshold value, and selecting the candidate area with the weighted value being greater than or equal to the preset threshold value as an optimal selection frame. When the preferred frame is obtained, the preferred frame is input into a basic residual error network (such as resnet50), the feature value of each convolutional network layer in the basic residual error network is calculated according to the preferred frame, and then the feature values corresponding to each layer are fused to obtain a feature pyramid. Inputting the preferred frame and the feature pyramid to a frame classification and regression detection network, calculating to obtain a regression detection result, pooling the regression detection result, and obtaining a verification classification result.

The automatic certification device for the student status provided by the embodiment realizes the end-to-end intelligent certification of the student status, improves the efficiency and the accuracy of the certification of the student status, and reduces the certification duration when a large batch of student status is certified.

In order to solve the technical problem, an embodiment of the present application further provides a computer device. Referring to fig. 4, fig. 4 is a block diagram of a basic structure of a computer device according to the present embodiment.

The computer device 6 comprises a memory 61, a processor 62, a network interface 63 communicatively connected to each other via a system bus. It is noted that only a computer device 6 having components 61-63 is shown, but it is understood that not all of the shown components are required to be implemented, and that more or fewer components may be implemented instead. As will be understood by those skilled in the art, the computer device is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction, and the hardware includes, but is not limited to, a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like.

The computer device can be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The computer equipment can carry out man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch panel or voice control equipment and the like.

The memory 61 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 61 may be an internal storage unit of the computer device 6, such as a hard disk or a memory of the computer device 6. In other embodiments, the memory 61 may also be an external storage device of the computer device 6, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the computer device 6. Of course, the memory 61 may also comprise both an internal storage unit of the computer device 6 and an external storage device thereof. In this embodiment, the memory 61 is generally used for storing an operating system installed in the computer device 6 and various application software, such as computer readable instructions of the automatic student authentication method. Further, the memory 61 may also be used to temporarily store various types of data that have been output or are to be output.

The processor 62 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data Processing chip in some embodiments. The processor 62 is typically used to control the overall operation of the computer device 6. In this embodiment, the processor 62 is configured to execute computer readable instructions stored in the memory 61 or process data, for example, execute computer readable instructions of the automatic membership authentication method.

The network interface 63 may comprise a wireless network interface or a wired network interface, and the network interface 63 is typically used for establishing a communication connection between the computer device 6 and other electronic devices.

The computer device provided by the embodiment realizes end-to-end intelligent authentication of the student status, improves the efficiency and accuracy of student status authentication, and reduces the authentication duration when large batches of student status are authenticated.

The present application further provides another embodiment, which is to provide a computer-readable storage medium storing computer-readable instructions executable by at least one processor to cause the at least one processor to perform the steps of the automatic cadaver authentication method as described above.

The computer-readable storage medium provided by this embodiment implements end-to-end intelligent authentication of the student status, improves the efficiency and accuracy of student status authentication, and reduces the authentication duration when authenticating a large batch of student status.

Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present application.

It is to be understood that the above-described embodiments are merely illustrative of some, but not restrictive, of the broad invention, and that the appended drawings illustrate preferred embodiments of the invention and do not limit the scope of the invention. This application is capable of embodiments in many different forms and is provided for the purpose of enabling a thorough understanding of the disclosure of the application. Although the present application has been described in detail with reference to the foregoing embodiments, it will be apparent to one skilled in the art that the present application may be practiced without modification or with equivalents of some of the features described in the foregoing embodiments. All equivalent structures made by using the contents of the specification and the drawings of the present application are directly or indirectly applied to other related technical fields and are within the protection scope of the present application.

Claims

1. An automatic student status authentication method is characterized by comprising the following steps:

2. The automatic certification method for cadastral nationalities according to claim 1, wherein the step of preprocessing the picture to be recognized to obtain a preprocessed picture comprises:

3. The automatic membership authentication method according to claim 2, wherein the step of identifying the table-line information in the picture to be identified based on the table-line identification model comprises:

4. The automatic membership authentication method according to claim 3, wherein after the step of obtaining table line information in the picture to be identified by performing regression detection on the feature pyramid and the preferred candidate box, the method further comprises:

5. The automatic membership authentication method according to claim 1, wherein said step of extracting text information in said preprocessed picture comprises:

6. The automatic membership authentication method as claimed in claim 1, wherein before the step of classifying and identifying the verification code pictures in the preprocessed pictures according to the verification code classification model, the method further comprises:

7. The automatic membership authentication method as claimed in claim 1, wherein said step of obtaining a trained captcha classification model further comprises:

8. An automatic student status authentication apparatus, comprising:

9. A computer device comprising a memory having computer readable instructions stored therein and a processor that when executed implements the steps of the automatic cadastral authentication method of any one of claims 1 to 7.

10. A computer-readable storage medium, having computer-readable instructions stored thereon, which, when executed by a processor, implement the steps of the automatic membership authentication method according to any one of claims 1 to 7.